DatriseAI-first ETL

Mixpanel Sisense

AI-first ETL from Mixpanel into Sisense. Governed entities, incremental sync, typed landing tables.

How Datrise loads Mixpanel into Sisense

Datrise syncs Mixpanel's events, user profiles, cohorts, funnels, and retention metrics into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.

Ideal for embedded analytics on an in-memory engine.

Endpoints

Mixpanel: Product analytics for events, funnels, and retention.

Sisense: Analytics platform with elastic data models and embedded analytics.

How Mixpanel entities map to Sisense

Mixpanel entitySisense objectNotes
eventsmixpanel_eventsdate/time fields events
user profilesmixpanel_user_profilesid PK · linked to mixpanel_events
cohortsmixpanel_cohortsid PK · linked to mixpanel_events
funnelsmixpanel_funnelsid PK · linked to mixpanel_events

FAQ

How does Datrise handle Mixpanel's custom fields in Sisense?

Flexible values are stored as flattened columns for the cube, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Sisense types.

How does the Mixpanel to Sisense sync stay up to date?

It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.

Related pipelines

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